Atmospheric aerosol inversion based on statistical and otsu segmentation

ABSTRACT

Embodiments include a method for retrieving atmospheric aerosol based on statistical segmentation. Firstly a multi-band remote sensing image including an apparent reflectance and an aerosol optical thickness look-up table corresponding to a retrieval band is obtained, then pixels are partitioned and screened according to apparent reflectance segments of a mid-infrared 2.1 micrometer band. After that the retained pixel sets are further partitioned and screened according to the apparent reflectance segments of the mid-infrared 1.6 micrometer band. Finally the obtained pixel sets are partitioned into two categories according to the pixel number, one category including pixels having more pixels, the other including those with less pixels. The category with more pixels is taken as the reference part for retrieval.

This application is a Continuation of U.S. application Ser. No.16/885,021, filed on May 27, 2021, which claims the benefit of theChinese Patent Application No. 201910454155.5, filed on May 29, 2019,which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates to the field of information remote sensingtechnologies, and more particularly to a method for retrievingatmospheric aerosol based on statistical segmentation.

BACKGROUND

The atmospheric aerosol is a multi-phase system formed by atmosphere aswell as solid and liquid particles suspending therein. It is a mixtureconsisting of molecular groups and liquid or solid particles suspendingin the air and having certain stability, a small sedimentation rate, anda size ranging from one micrometer to dozens of micrometers. Aerosolparticles mainly originate from artificial sources such as industrialactivities, biological burning and the like, as well as natural sourcessuch as dusts, offshore marine particles and the like.

Research shows that the atmospheric aerosol not only affects radiationbalance of an earth surface layer system, but also causes theearth-atmosphere system to cool by adjusting a reflectivity of an earthatmosphere system through scattering solar shortwave radiation. It alsocauses a heating process through absorbing the solar radiation, andchanges microphysical properties of clouds by affecting themicrophysical process of formation of the clouds. Aerosol alsosignificantly influences atmospheric chemical processes andbiogeochemical cycle. At the same time, the aerosol particles containsome particles harmful to human body, which may cause serious problem tothe health of human beings, especially in heavily populated urban areaswith many industries. Therefore, retrieval of the atmospheric aerosolhas a significant meaning to research of global climate change as wellas monitoring and management of atmospheric pollution.

Currently, researchers home and abroad have made quite some achievementsin terms of aerosol remote sensing retrieval. They also pay muchattention to monitoring of the aerosol in designing novel sensors.However, most of the conventional retrieval algorithms are directed tolarge-size aerosol having average properties, and quite limited in termsof applications. For example, such algorithms are only adapted to darkground surface. At present, there is no effective aerosol retrievalalgorithm adapted to bright ground surface areas such as cities.

SUMMARY

An object of the present invention is to provide a method for retrievingatmospheric aerosol based on statistical segmentation. Two parts arepartitioned by way of statistical segmentation, and aerosol thicknessvalues thereof are retrieved using different methods, thereby improvingaccuracy and resolution of the retrieval result of the bright groundsurface area.

In order to meet the above-mentioned objective of the present invention,the method for retrieving atmospheric aerosol based on statisticalsegmentation in the present invention comprises steps of:

S1: acquiring a multi-band high-resolution remote sensing image of ananalyzed area using satellites, and performing radiometric calibrationto obtain a multi-band remote sensing image comprising an apparentreflectance, wherein the multi-band remote sensing image comprises amid-infrared 1.6 micrometer band and a mid-infrared 2.1 micrometer band,and selecting a blue/costal band from the multi-band remote sensingimage as an retrieval band;

S2: inputting the multi-band remote sensing image into an atmosphericradiative transfer model, obtaining a corresponding aerosol opticalthickness look-up table according to the retrieval band, wherein thelook-up table comprises parameters corresponding to each aerosol opticalthickness value AOT for calculating the apparent reflectance;

S3: for all pixels in the multi-band remote sensing image, performingcategorization using a statistical segmentation method, wherein thesegmentation method comprises steps of:

S3.1: partitioning an apparent reflectance range of the mid-infrared 2.1micrometer band into N segments φ_(n), n=1, 2, . . . , N, according to apredetermined interval λ₁;

S3.2: based on apparent reflectances of respective pixels in themulti-band remote sensing image in the mid-infrared 2.1 micrometer band,allocating the respective pixels into corresponding segments, to obtainpixel sets x_(n) corresponding to respective segments φ_(n);

-   -   x_(n) obtained in step S3.2, in condition that a number of        pixels in the pixel set x_(n)|x_(n)|≥T₁, T₁        otherwise deleting the pixel set; marking retained pixel sets as        x_(n′), n′=1, 2, . . . , N′, N′ being a number of retained pixel        sets;

S3.4: partitioning an apparent reflectance range of the mid-infrared 1.6micrometer band into M segments γ_(m), m=1, 2, . . . , M according to apredetermined interval λ₂;

S3.5: based on apparent reflectances of pixels in each pixel set x_(n′)in the mid-infrared 1.6 micrometer band, allocating respective pixels toa corresponding segment, to obtain pixel sets y_(n′,m) corresponding torespective segments γ_(m) in the pixel sets;

S3.6: in condition that a number of pixels in the pixel sety_(n′,m)|y_(n′,m)|≥T₂, T₂ being a predetermined threshold value,retaining the pixel set γ_(n′,m), otherwise deleting it; markingretained pixel sets as y_(k), k=1, 2, . . . , K, K being a number of theretained pixel sets;

S3.7: when an apparent reflectance of a pixel in the pixel set y_(k) inthe blue/coast band ρ_(y) _(k,i) <T, i=1, 2, . . . , |y_(k)|, |y_(k)|being the number of pixels in the set y_(k), T being an OTSU thresholdof the apparent reflectance of the pixel set y_(k) in the blue/coastband, allocating the pixel into a target set Y_(s), otherwise allocatingthe pixel to an error categorization set Y_(t);

S4: for the target set Y_(s), acquiring the aerosol optical thicknessvalue AOT of each pixel through the steps of:

S4.1: marking a p-th pixel set in the set Y_(s) as y_(s,p), p=1, 2, . .. , |Y_(s)|, |Y_(s)| being a number of pixel sets in the set Y_(s), foreach pixel set y_(s,p), acquiring a pixel y_(s,p,min) with the minimumapparent reflectance in the blue/coast band, the pixel y_(s,p,min) beinga clean pixel;

S4.2: setting an aerosol optical thickness value AOT of the clean pixelas ε, ε being a preset minimum value, retrieving a ground surfacereflectivity of the clean pixel t_(s,p,min) from the aerosol opticalthickness look-up table generated in S2; setting ground surfacereflectivities of all pixels in the pixel set y_(s,p) as being equal tothe ground surface reflectivity of the clean pixel y_(s,p,min);

S4.3: retrieving aerosol optical thickness values AOTs of all pixels inthe target set Y_(s).

S5: performing retrieval on the error categorization set Y_(t) throughthe steps of:

S5.1: traversing all pixels in the set Y_(s), to search for pixels withno values within a predetermined radius, designating an aerosolthickness value of a pixel with no value as the aerosol thickness valueof the pixel, and marking a set comprising all designated pixels asP_(s);

S5.2: for each pixel in the set P_(s), obtaining parameterscorresponding to the designated aerosol optical thickness value from theaerosol optical thickness look-up table, and then calculating andobtaining a ground surface reflectivity of the pixel according to theapparent reflectance;

S5.3: gridding the multi-band remote sensing image according to apredetermined side length;

S5.4: marking a q-th pixel set in the set Y_(t) as y_(t,q), q=1, 2, . .. , |Y_(t)|, |Y_(t)| being a number of the pixel sets in the set Y_(t);for each pixel set y_(t,q), finding an intersection with P_(s) from eachgrid partitioned in step S5.3, and setting the ground surfacereflectivity of all pixels of the pixel set y_(t,q) in a current grid asbeing equal to an average ground surface reflectivity value of allpixels in the intersection;

S5.5: performing aerosol thickness value retrieval on each pixelassigned with the ground surface reflectivity in the step S5.4, toobtain the aerosol thickness value of the pixel.

In the method for retrieving atmospheric aerosol based on statisticalsegmentation in the present invention, the aerosol optical thicknesslook-up table of the retrieval band are obtained first, then the pixelsare partitioned and screened according to the apparent reflectancesegments of the mid-infrared 2.1 micrometer band. After that theretained pixel sets are further partitioned and screened according tothe apparent reflectance segments of the mid-infrared 1.6 micrometerband. Finally the obtained pixel sets are partitioned into twocategories according to the respective OTSU threshold for apparentreflectance in the coast band, with pixels smaller than the threshold inone category, and the remaining pixels in another category. The categorywith pixels smaller than the threshold is taken as the target set forretrieval. Specifically, each of the pixel sets are first searched forthe clean pixel, then the ground surface reflectivity of the clean pixelis taken as the ground surface reflectivity of the whole pixel set,thereby obtaining the aerosol optical thickness value through retrieval.After that these pixels are taken as references to perform retrieval onthe other category.

The present invention adopts a novel method when calculating the groundsurface reflectivity. According to this method, the ground surfacereflectivity is determined according to the clean pixel, instead ofdepending on a dark pixel. Therefore, as long as completion of thestatistical segmentation is satisfied, the retrieval can be performed.Consequently, the present invention also has good retrieval effect forbright ground surface area, and has wider applicability compared withthe conventional dark pixel algorithm.

BRIEF INTRODUCTION OF THE ACCOMPANYING DRAWINGS

FIG. 1 schematically illustrates a flow chart of a method for retrievingatmospheric aerosol based on statistical segmentation in accordance withan embodiment of the present invention;

FIG. 2 is a diagram of an image used in the present example;

FIG. 3 schematically illustrates a flow chart of statisticalsegmentation in accordance with an embodiment of the present invention;

FIG. 4 schematically illustrates partitioning pixels of Band 7 inaccordance with an embodiment of the present invention;

FIG. 5 schematically illustrates spectral curves of ground surfacereflectivity;

FIG. 6 is a partial histogram of an apparent reflectance of a pixel sety_(k) in Band 1.

FIG. 7 schematically illustrates a flow chart of performing retrieval oneach pixel set y_(s,p) in the set Y_(s);

FIG. 8 schematically illustrates a flow chart of performing retrieval onthe set Y_(t);

FIG. 9 schematically illustrates a gridding result of the image shown inFIG. 2; and

FIG. 10 schematically illustrates a result of retrieving a multi-bandremote sensing image shown in FIG. 2 according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following, specific embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings suchthat a person skilled in the art can better understand the invention. Inthe following description, it is noted that well-known functions andconfigurations are not described in detail to avoid obscuring thepresent invention.

Embodiments

FIG. 1 is a flow chart of a method for retrieving atmospheric aerosolbased on statistical segmentation according to an embodiment of thepresent invention. As shown in FIG. 1, the method for atmosphericaerosol retrieval based on statistical segmentation comprises thefollowing steps:

S101: acquiring a multi-band remote sensing image.

A multi-band high-resolution remote sensing image of an analyzed area isacquired through satellites, and radiometric calibration is performed toobtain a multi-band remote sensing image comprising an apparentreflectance. The multi-band remote sensing image comprises amid-infrared 1.6 micrometer band and a mid-infrared 2.1 micrometer band,and a blue/coast band is selected from the multi-band remote sensingimage as a retrieval band.

Currently the LANDSAT satellite is used to acquire the multi-bandhigh-resolution remote sensing image. In a LANDSAT 8 OLI image there are11 bands, namely, Band1 costal band, Band2 blue band, Band3 green band,Band4 red band, Band5 near-infrared band, Band6 mid-infrared 1.6micrometer band, Band7 mid-infrared 2.1 micrometer band, Band8full-color band, Band9 cirrus band, Band10 thermal infrared band 1, andBand11 thermal infrared band 2. Band6 mid-infrared band and Band7mid-infrared band are used in the present invention. The retrieval bandselected in the present example is Band1, this is because Band1 in theLANDSAT 8 OLI image is more sensitive to the aerosol, and can thereforeimprove the retrieval accuracy.

An image taken on the 42^(nd) day of the year 2015 in Beijing is used inthe present example. FIG. 2 is the image used in the present example. Asshown in FIG. 2, this image includes bright ground surface areas (withrelatively light-color).

S102: acquiring an aerosol optical thickness (AOT) look-up table.

The multi-band remote sensing image obtained in step S101 is input intoan atmospheric radiative transfer model, thereby arriving at acorresponding aerosol optical thickness look-up table according to theretrieval band. The look-up table includes parameters corresponding toeach aerosol thickness value AOT for calculating the apparentreflectance. In the present example, assuming that an underlying surfaceis a Lambertian surface, its apparent reflectance ρ_(TOA) is calculatedas:

$\rho_{TOA} = {\rho_{0} + {\frac{T_{s} \cdot T_{v} \cdot \rho_{s}}{1 - {\rho_{s} \cdot S}}.}}$

where ρ₀ is an atmospheric path radiation reflectivity, ρ_(s) is aground surface reflectivity, S is a downward hemispherical emissivity ofa lower atmospheric layer, T_(s) is a total transmittance of incidentlight from top of the atmosphere to the ground surface, T_(v) is a totaltransmittance of light upwardly entering a satellite sensor field.Therefore it can be seen that in the present example the parameterswhich need to be included in the look-up table include the atmosphericpath radiation reflectivity, the atmosphere hemispherical emissivity,and a product of the total transmittance of the incident light from thetop of the atmosphere to the ground surface and the total transmittanceupwardly entering a satellite sensor field.

Currently, there are many atmospheric radiative transfer models. In theembodiment, the most common 6S radiative transfer model is used. Inaddition to the multi-band remote sensing image, other parameters thatneed to be input into the 6S radiative transfer model include: date,zenith angle, and azimuth angle corresponding to the multi-band remotesensing image, an aerosol type and atmosphere mode, a step size of theaerosol optical thickness value and so on. The atmosphere mode selectedin the present example is mid-latitude winter, the aerosol type is acontinental type, and the selected retrieval band is Band1 costal band.Table 1 is a look-up table acquired in the present example.

TABLE 1 AOT P₀ S T_(s) · T_(v) 0.01 0.07883 0.78547 0.13188 0.02 0.081030.77265 0.13571 . . . . . . . . . . . . 2.99 0.24536 0.06927 0.29881

S103: performing statistical segmentation on the pixels.

For all pixels in the multi-band remote sensing image, segmentation isperformed using a statistical segmentation method. As the mid-infrared1.6 micrometer band and mid-infrared 2.1 micrometer band have relativelylong wavelengths, and they are not susceptible to aerosol interference.Therefore, the apparent reflectances of the mid-infrared 1.6 micrometerband and the mid-infrared 2.1 micrometer band are used to segment thepixels in the present invention, and aerosol optical thickness valueretrieval is performed with the costal band. Since the LANDSAT 8 OLIimage is used in the present example, the mid-infrared 1.6 micrometerband is Band6, the mid-infrared 2.1 micrometer band is Band1, and thecostal band is Band1.

FIG. 3 is a flow chart of categorizing pixels in accordance with anembodiment of the present invention. As shown in FIG. 3, categorizingpixels in the present invention comprises the following steps.

S301: partitioning the apparent reflectance of Band 7 into segments.

The apparent reflectance range of Band7 is partitioned into N segmentsφ_(n), n=1, 2, . . . , N, according to a predetermined interval λ₁. Thepartitioning interval λ₁ can be determined as needed. Generally, theinterval λ₁ is within a range of 0.002≤λ₁≤0.01.

S302: allocating the pixels.

Based on apparent reflectances in Band7 of respective pixels of themulti-band remote sensing image, the pixels are allocated tocorresponding segments, to obtain pixel sets x_(n) corresponding torespective segments φ_(n). Assuming that an apparent reflectance of apixel at Band7 is marked as ρ, then a serial number of its correspondingsegment will be ┌ρ/λ┘, ┌ ┘ is rounding up to an integer.

FIG. 4 is a diagram illustrating partitioning part of the pixels ofBand7 at an interval of 0.005 in the present example. FIG. 4(a) is anoverall diagram of the partitioning, and FIG. 4(b) is a partial diagramof the partitioning. As shown in FIG. 4, by partitioning the apparentreflectance range of Band7 into segments, the pixels in the multi-bandremote sensing image can be partitioned into segments.

S303: screening pixel sets.

N pixel sets x_(n) obtained in step S302 are evaluated one by one. If anumber of pixels in the pixel set x_(n) |x_(n)|≥T₁, T₁ being apredetermined threshold value, this pixel set is retained, otherwise itis deleted. The retained pixel sets are marked as x_(n′), n′=1, 2, . . ., N′, N′ is the number of the pixel sets retained after the screening.The value of the threshold T₁ may also be set as needed. Apparently, thehigher the resolution of the multi-band remote sensing image and thelarger the interval λ₁ is, and the more pixels are included in eachsegment. Correspondingly, the threshold value T₁ may also be set to beslightly higher, otherwise the threshold value T₁ may be set to beslightly lower. In the present example, T₁=100000.

S304: partitioning the apparent reflectance of Band6 into segments.

The apparent reflectance range of Band6 is partitioned into M segmentsγ_(m), m=1, 2, . . . , M, according to a predetermined interval λ₂.Likewise, the partitioning interval λ₂ can be determined as needed, andmay be within the range of 0.002≤λ₂≤0.01.

S305: further partitioning the pixel sets.

Each pixel set x_(n′) retained in step S303 is further partitioned. Thefurther partitioning may be performed in the following way: each pixelwithin each pixel set x_(n′) is allocated into a corresponding segmentγ_(m) according to an apparent reflectance of the pixel at Band6, toobtain a pixel set γ_(n′,m) in corresponding to respective segment γ_(m)in this pixel set. Apparently, after further partitioning the pixelsets, N′×M pixel sets in total are obtained.

S306: re-screening the pixel sets.

The N′×M pixel sets obtained in step S305 are screened again. If anumber of pixels in the pixel set y_(n′,m) |y_(n′,m)|≥T₂, T₂ being apredetermined threshold value, this pixel set is retained, otherwise itis deleted. The pixel set retained after the screening is marked asy_(k), k=1, 2, . . . , K, K is the number of the pixel sets retainedafter the screening. Like the threshold value T₁, the threshold value T₂may also be set as needed. Since the pixel set y_(n′,m) is a pixel setobtained after further partitioning, the number of pixels in the pixelset y_(n′,m) will definitely be less, therefore T₂ is definitely smallerthan T₁. In the present example, T₂=5000.

S307: categorizing the pixel sets in Band1.

The K pixel sets y_(k) obtained after screening in step S 306 arecategorized according to an OTSU threshold T and an apparent reflectanceof each set y_(k) in Band1. If a pixel in the pixel set y_(k) has anapparent reflectance ρ_(y) _(k,i) <T, i=1, 2, . . . , |y_(k)|, where|y_(k)| denotes a number of pixels in the pixel set y_(k), the pixely_(k,i) is categorized into a target set Y_(s), otherwise it iscategorized into an error categorization set Y_(t).

The pixels in the multi-band remote sensing image are segmentedaccording to the apparent reflectances of Band6 and Band1 through theabove steps, and get the final categorization result in Band1. FIG. 5schematically illustrates spectral curves of ground surfacereflectivities. FIG. 5 shows spectral curves of ground surfacereflectivities of some objects in an ENVI standard spectrum database. Itcan be seen by studying spectral curves of a lot of objects that thesame or different objects may have different ground surface reflectivitycurves under different conditions, and the reflectivity curves mayoverlap with each other at the mid-infrared 1.6 micrometer band and themid-infrared 2.1 micrometer band. However, it rarely happens like this,especially for regular objects, and the categorization error can befurther reduced by the subsequent OTSU threshold method. FIG. 6 is apartial histogram of the apparent reflectance of the pixel set inBand 1. FIG. 6 shows that after twice segmentation, the histogram of theapparent reflectance of the pixels in the pixel set in Band1 has amultivariate normal distribution, and there is a clear highest peak. Thestudy has found that the pixels corresponding to the highest peak arecorrectly categorized pixels, and the remaining short peaks areerroneously categorized pixels. Therefore, these two types of pixels areseparated by the OTSU threshold method. Therefore, although there may beerrors in the categorization method used in the present invention, thepixels in the multi-band remote sensing image can still be partitionedinto many segments using the mid-infrared 1.6 micrometer band and themid-infrared 2.1 micrometer band, and the result is further improved bythe OTSU threshold method. If no error exists in these categorizationresults, eventually, the pixels belonging to the same segment shouldhave approximately the same ground surface reflectivity curve, and theirground surface reflectivities at the retrieval band should beapproximately equal.

S104: performing retrieval on the sets Y_(s).

In the present invention, the sets Y_(s), taken as a reference set, areprocessed using a method of retrieving polluted pixels based on cleanpixels.

In the retrieval of aerosol, it is very important to obtain the groundsurface reflectivity. It is shown by experiments that each pixel segmentin the target sets Y_(s) has few errors, this is mainly because eachpixel set in the target sets Y_(s) is the same type of regular object,for example, vegetation and bare earth and so on. In the meantime, someerrors can be further eliminated by the OTSU threshold method. Moreover,the probability that each pixel set in Y_(s) containing clean pixels isrelatively high. The p-th pixel set in Y_(s) is denoted as y_(s,p), p=1,2, . . . , |Y_(s)|, |Y_(s)| being a number of the pixel sets in the setsY_(s). Thus, as long as the ground surface reflectivities of the cleanpixels in each pixel set are obtained, the ground surface reflectivitiesof all pixels in the pixel set can be obtained. Therefore, it isnecessary to obtain the ground surface reflectivities of the cleanpixels in each pixel set first.

FIG. 7 is a diagram illustrating a flow chart of retrieving each pixelset y_(s,p) in the sets Y_(s). As shown in FIG. 7, a detailed method forretrieving the pixel set y_(s,p) is as follows.

S701: searching for a clean pixel.

For each pixel set y_(s,p) in the set Y_(s), a pixel with the minimumapparent reflectance in Band1 is searched out. The minimum value cannotbe less than or equal to 0, and it is recorded as a clean pixely_(s,p,min).

S702: calculating the ground surface reflectivity.

Assuming that the aerosol thickness value AOT of the clean pixely_(s,p,min) is ε, where ε is a preset minimum value, various parameterscorresponding to the aerosol optical thickness value ε are searched outfrom the aerosol optical thickness look-up table generated in S102. Theground surface reflectivity is calculated based on the apparentreflectance of the clean pixel y_(s,p,min). In the present embodiment,ε=0.01.

S703: performing retrieval on the set y_(s,p).

It can be seen from the previous analysis that the ground surfacereflectivity corresponding to the clean pixel y_(s,p,min) is actuallythe ground surface reflectivity of the whole pixel set y_(s,p).Therefore, it can be used to retrieve the aerosol optical thicknessvalue of each pixel in the pixel set y_(s,p). The retrieval method maybe selected as needed. A retrieval method used in the present example isas follows:

obtaining parameters corresponding to each aerosol thickness value AOTfrom the look-up table, and calculating an apparent reflectance ρ_(i)corresponding to each aerosol thickness value AOT, where i=1, 2, . . . ,L, L is the number of the aerosol thickness value in the look-up table.

For each pixel, an apparent reflectance closest to its apparentreflectance is searched from the L apparent reflectances ρ_(i), and anaerosol thickness value AOT corresponding thereto is just the aerosolthickness value of this pixel.

S105: performing retrieval on the set Y_(t).

-   -   Y_(s) in step S104 to obtain the aerosol optical thickness value        of each pixel, retrieval for the pixels in the set Y_(t) is        performed by taking the pixels in the set Y_(s) as references.        FIG. 8 is a flow chart of performing retrieval on the set Y_(t).        As shown in FIG. 8, detailed steps of performing retrieval on        the set Y_(t) are as follows.

S801: expanding reference pixels.

Each pixel in the set Y_(s) is traversed, to search for pixels with novalue within a predetermined radius. The aerosol thickness value of thispixel is designated as aerosol thickness values of the pixels with novalue. A pixel set of all filled and assigned pixels is marked as P_(s).The searching radius r is generally set as needed, and the smaller theradius is, the more precise the filled aerosol thickness value will be,though the number of pixels remained with no value will be increased.Generally, the searching radius r is within the range of 1≤r≤10.

S802: calculating the ground surface reflectivities of the pixels in theset P_(s).

For each pixel in the set P_(s), parameters corresponding to the filledaerosol thickness value are obtained from the aerosol optical thicknesslook-up table, and then the ground surface reflectivity of this pixel iscalculated and obtained according to the apparent reflectance.

S803: gridding the multi-band remote sensing image.

Considering that the objects have certain correlation in geographicspace, the multi-band remote sensing image is partitioned into gridsaccording to a predetermined side length. The predetermined side lengthcan be set according to a resolution of the multi-band remote sensingimage. FIG. 9 is a gridding result of the image shown in FIG. 2.

S804: acquiring the ground surface reflectivity.

A q-th pixel set in the set Y_(t) is marked as y_(t,q), q=1, 2, . . . ,|Y_(t)|, |Y_(t)| representing the number of the pixel sets in the setY_(t). For each pixel set y_(t,q), an intersection with P_(s) isobtained for each grid partitioned in step S803. Then the ground surfacereflectivity of all pixels of the pixel set y_(t,q) in the current gridis set to be equal to an average ground surface reflectivity value ofall pixels in this intersection. Thus, the ground surface reflectivitiesof the pixels in the set Y_(t) are obtained.

S805: retrieving the aerosol thickness value.

Aerosol thickness value retrieval is performed for each pixel assignedwith the ground surface reflectivity in step S804, to obtain the aerosolthickness value of this pixel.

Since the pixel sets are screened in step S103 in the present invention,the sets Y_(s) and Y_(t) do not contain all pixels of the multi-bandremote sensing image, neither can retrieval be performed on all of thepixels in step S105. A number of the remaining pixels with no value canbe reduced by adjusting the threshold of each segment or adjusting thegrid size, so the remaining pixels with no value have little effect onthe overall retrieval result. FIG. 10 schematically illustrates aretrieval result for the multi-band remote sensing image shown in FIG. 2using the present invention, in which the blank portion representspixels with no value or water pixels. It can be seen from the resultshown in FIG. 10 that the present invention also has good retrievaleffect for bright ground surface areas, and therefore has a widerapplicability compared with the conventional dark pixel algorithm.

In order to prove the accuracy of the present invention, data of AERONET(Aerosol Robotic Network) ground observation network is downloaded fromthe website http://aeronet.gsfc.nasa.gov/. The retrieval results of thepresent invention are compared with the ground observation data and theretrieval results of the dark pixel algorithm, and two selected sitesare both located in the city, i.e. belonging to bright ground surfaceareas. Table 2 shows comparison of the retrieval results of the presentinvention, the dark pixel algorithm retrieval results and the groundsurface observation data of the graph as shown in FIG. 2.

TABLE 2 Site Beijing Beijing-RADI Observation data 0.1393 0.1575Retrieval result of dark 0.66 0.64 pixel method Retrieval result of the0.21 0.47 present invention Error of the dark pixel 0.5207 0.4825 methodError of the present 0.0707 0.3125 invention

As shown in Table 2, the retrieval results of the present invention arecloser to the observation data of the actual sites, and their errors aresmaller. It can be seen that for the bright ground surface area, thepresent invention has good retrieval results. Therefore compared withthe dark pixel method, the present invention has a wider applicationscope.

The above is only the preferred embodiments of the invention and is notintended to limit the invention. For a person skilled in the art, theinvention may have a variety of changes and modifications. Any change,equivalent replacement, improvement made within the spirit and principleof the present invention should be included in the protection scope ofthe invention.

1. A method for retrieving atmospheric aerosol based on statisticalsegmentation, the method comprising: (S1) acquiring a multi-bandhigh-resolution remote sensing image of an analyzed area usingsatellites, and performing radiometric calibration to obtain amulti-band remote sensing image comprising an apparent reflectance,wherein the multi-band remote sensing image comprises a mid-infrared 1.6micrometer band and a mid-infrared 2.1 micrometer band, and selecting ablue/costal band from the multi-band remote sensing image as anretrieval band; (S2) inputting the multi-band remote sensing image intoan atmospheric radiative transfer model, obtaining a correspondingaerosol optical thickness look-up table according to the retrieval band,wherein the look-up table comprises parameters corresponding to eachaerosol optical thickness value AOT for calculating the apparentreflectance; (S3) for all pixels in the multi-band remote sensing image,performing categorization using a statistical segmentation method,wherein the segmentation method comprises the steps of: (S3.1)partitioning an apparent reflectance range of the mid-infrared 2.1micrometer band into N segments φ_(n), n=1, 2, . . . , N, according to apredetermined interval λ₁; (S3.2) based on apparent reflectances ofrespective pixels in the multi-band remote sensing image in themid-infrared 2.1 micrometer band, allocating the respective pixels intocorresponding segments, to obtain pixel sets x_(n) corresponding torespective segments φ_(n); (S3.3) for each pixel set x_(n) obtained instep S3.2, in condition that a number of pixels in the pixel set x_(n)|x_(n)|≥T₁, T₁ being a predetermined threshold value, retaining thepixel set, otherwise deleting the pixel set; marking retained pixel setsas x_(n′), n=1, 2, . . . , N′, N′ being a number of retained pixel sets;(S3.4) partitioning an apparent reflectance range of the mid-infrared1.6 micrometer band into M segments γ_(m), m=1, 2, . . . , M, accordingto a predetermined interval λ₂; (S3.5) based on apparent reflectances ofpixels in each pixel set x_(n′) γ_(n′,m) γ_(m) in the pixel sets; (3.6)in condition that a number of pixels in the pixel set y_(n′,m)|y_(n′,m)|≥T₂, T₂ being a predetermined threshold value, retaining thepixel set y_(n′,m), otherwise deleting it; marking retained pixel setsas y_(k), k=1, 2, . . . , K, K being a number of the retained pixelsets; (S3.7) when an apparent reflectance of a pixel in the pixel sety_(k) in the blue/coast band ρ_(y) _(,k,i) <T, i=1, 2, . . . , |y_(k)|,|y_(k)| being the number of pixels in the set y_(k), T being an OTSUthreshold of the apparent reflectance of the pixel set y_(k) in theblue/coast band, allocating the pixel into a target set Y_(s), otherwiseallocating the pixel to an error categorization set Y_(t); (S4) for thetarget set Y_(s), acquiring the aerosol optical thickness value AOT ofeach pixel through the steps of: (S4.1) marking a p-th pixel set in theset Y_(s) as y_(s,p), p=1, 2, . . . , |Y_(s)|, |Y_(s)| being a number ofpixel sets in the set Y_(s), for each pixel set y_(s,p), acquiring apixel y_(s,p,min) with the minimum apparent reflectance in theblue/coast band, the pixel y_(s,p,min) being a clean pixel; (S4.2)setting an aerosol optical thickness value AOT of the clean pixel as ε,ε being a preset minimum value, retrieving a ground surface reflectivityof the clean pixel y_(s,p,min) from the aerosol optical thicknesslook-up table generated in step S2; setting ground surfacereflectivities of all pixels in the pixel set y_(s,p) as being equal tothe ground surface reflectivity of the clean pixel y_(s,p,min); (S4.3)retrieving aerosol optical thickness values AOTs of all pixels in thetarget set Y_(s); (S5) performing retrieval on the error categorizationset Y_(t) through the steps of: (S5.1) traversing all pixels in the setY_(s), to search for pixels with no values within a predeterminedradius, designating an aerosol thickness value of a pixel with no valueas the aerosol thickness value of the pixel, and marking a setcomprising all designated pixels as P_(s); (S5.2) for each pixel in theset P_(s), obtaining parameters corresponding to the designated aerosoloptical thickness value from the aerosol optical thickness look-uptable, and then calculating and obtaining a ground surface reflectivityof the pixel according to the apparent reflectance; (S5.3) gridding themulti-band remote sensing image according to a predetermined sidelength; (S5.4) marking a q-th pixel set in the set Y_(t) as y_(t,q),q=1, 2, . . . , |Y_(t)|, |Y_(t)| being a number of the pixel sets in theset Y_(t); for each pixel set y_(t,q), finding an intersection withP_(s) from each grid partitioned in step S5.3, and setting the groundsurface reflectivity of all pixels of the pixel set y_(t,q) in a currentgrid as being equal to an average ground surface reflectivity value ofall pixels in the intersection; (S5.5) performing aerosol thicknessvalue retrieval on each pixel assigned with the ground surfacereflectivity in the step S5.4, to obtain the aerosol thickness value ofthe pixel.
 2. The method for retrieving atmospheric aerosol based onstatistical segmentation according to claim 1, wherein the interval λ₁in step S3.1 is within a range of 0.002≤λ₁≤0.01.
 3. The method forretrieving atmospheric aerosol based on statistical segmentationaccording to claim 1, wherein the interval λ₂ in step S3.4 is within arange of 0.002≤λ₂≤0.01.
 4. The method for retrieving atmospheric aerosolbased on statistical segmentation according to claim 1, wherein theminimum value ε in step S4.2 is 0.01.
 5. The method for retrievingatmospheric aerosol based on statistical segmentation according to claim1, wherein the retrieving method of the ground surface reflectivity instep S4.2 and step S5.2 is: searching for respective parameterscorresponding to the aerosol optical thickness value AOT of a pixel fromthe aerosol optical thickness look-up table, and calculating the groundsurface reflectivity based on the apparent reflectance of the pixel. 6.The method for retrieving atmospheric aerosol based on statisticalsegmentation according to claim 1, wherein in step S4.3 and step S5.5the aerosol thickness value of each pixel is retrieved through the stepsof: obtaining parameters corresponding to each aerosol optical thicknessvalue AOT from the look-up table, and calculating an apparentreflectance ρ_(i) corresponding to each aerosol thickness value AOT,where i=1, 2, . . . , L, L being the number of the aerosol thicknessvalue in the look-up table; for each pixel, searching for an apparentreflectance closest to its own apparent reflectance from L apparentreflectances ρ_(i), and setting a corresponding aerosol opticalthickness value AOT as being the aerosol thickness value of the pixel.